In various forms of networked or otherwise distributed data processing systems, complex and/or multiple related processes are often routed to multiple computing resources for distribution, dissemination or execution.
For example, it has been observed that release of data signals representing information content such as market data related to the trading of various financial instruments may also have significant effects on markets such as exchanges, and in other forms of social and/or economic interchange.
The financial instruments may include, for example, various types of securities, equities, bonds, futures, options contracts, various types of derivatives, and so forth.
Market data may include, for example, but is not limited to: order acknowledgements; order executions; order confirmations; open orders; market orders; basic market data or level 1 data such as bid price, bid size, ask price, ask size, last price, and last size; volume information; additional market data or level 2 data, such as market data that relates to order book or depth of the market including highest bid prices that traders are willing to pay to buy a security (e.g. 5 or 10 prices depending on the market), bid sizes (e.g. the number of securities that are available at each of the highest bid prices); lowest ask prices that traders are willing to accept to sell a security; ask prices or the number of securities that are available at each of the lower ask prices; and next best bid; quotes; open interest (e.g. the number of buy market orders before the stock market opens); change in open interest; exchange of contract for related position (ECRP) volume; the number of securities available for sale; the number of securities sought to be purchased at each offer and bid price; information regarding cancelled, non-published, non-completed and/or unexecuted orders; calculated market data (e.g. imbalances and indices) and other price information.
Market data may also include aggregation of various elements of market data; such as a aggregated set of trading volume information. The market data may be publicly available; or non-publicly available.
Other types of market data may also be contemplated by a skilled person.
Market data may be provided by and/or generated from a variety of sources. For example, sources of market data may include, but are not limited to, backend order management systems; order fulfillment systems; exchanges; exchange matching engines (processing buy/sell orders and applying different rules of different exchanges); equity trading facilities such as alternative display facilities (ADFs), for example, the ADF of the U.S. Financial Industry Regulatory Authority); electronic communication networks (ECNs) for matching buy and sell orders, used, for example, to connect brokers to individual traders for processing orders; security information processors (SIPs) for carrying trade and order information; trade reporting services (TRS); and transaction reporting services (e.g. Euronext).
Recipients of market data may include a variety of stakeholders, some of whom use the market data for various purposes upon receipt. These recipients may include, but are not limited to: institutional traders; brokerages; high frequency traders; retail investors; individual traders; exchanges (because they often consume the data of other exchanges); alternative trading systems (such as “black pools”); analysts, industry regulators, and market data providers or market data vendors.
Market data may generally be provided in the form of raw data, communicated as network packets. In some embodiments, market data may also be in various formats, such as media files and/or with formatting applied.
Generally, market data may be sent shortly after it is generated, and as continuously as possible, subject to consolidation rules for consolidated streams. Normally, each source of market data operates a data service implemented using “send rules” based on continuous sending of market data.
The timing of the release of market data, such as order confirmations and other information content data sets can be of significant, and even crucial importance. For example, even small differences in the timing of availability of information represented by such content can be used to advantage, fairly or unfairly, by those who are in a position to act upon it first. In such cases it can be advantageous to ensure that the availability or distribution of such information to various parties, such as market participants, is synchronized or otherwise controlled, so as, for example, to prevent unfair or unjust exploitation of the content.
Thus, as will be apparent to those skilled in the relevant arts, it can be advantageous in many situations to synchronize or otherwise control routing of signals representing market data and/or other information content to pluralities of networked computing resources, so as to permit, or otherwise enable, such signal data sets to arrive, or otherwise be available for interpretation, execution, and or other processing, by such networked computing resources simultaneously, or according to other desired synchronization schemes, as described herein. For example, it can be advantageous, or otherwise desirable, to cause information content data sets representing news or other information to arrive at such networked computing resources in such fashion as to enable the networked computing resources to access, parse, interpret, and display or otherwise process content represented by such signals in synchronized fashion. Such processing can, for example, include fully- or semi-automated parsing or analysis of such content, for manual and/or fully- or semi-automated interpretation, or other use, of the information represented by such content. For example, such synchronization can be used to control manual and/or fully- or semi-automated interpretation of such content in subsequent generation, routing, cancellation, and/or other execution or processing of data sets representing proposed transactions in financial interests.
Prior art documents, such as the Rony Kay article “Pragmatic Network Latency Engineering, Fundamental Facts and Analysis, have attempted to address problems such as those described above by proposing elimination of one-way communications (i.e., “packet”) latencies. Such systems fail to address arbitrage opportunities and other issues caused or facilitated by variations in the time required for multiple processors to execute individual portions of multiple-processor execution requests (i.e., execution latencies), in addition to (or as part of) communications latencies.